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This study aims to address the limited understanding of the complex correlations among strut size, structural orientation and process parameters in selective laser melting (SLM)-fabricated lattice structures. By investigating the effects of crucial process parameters, strut diameter and angle on the microstructure and mechanical performance of AlSi10Mg struts, the research seeks to enhance the surface morphologies, microstructures and mechanical properties of AM lattice structures, enabling their application in various engineering fields, including medical science and space technologies.

This comprehensive study investigates SLM-fabricated AlSi10Mg strut structures, examining the effects of process parameters, strut diameter and angle on densification behavior and microstructural characteristics. By analyzing microstructure, geometrical properties, melt pool morphology and mechanical properties using optical microscopy, scanning electron microscope, energy dispersive X-ray spectroscopy and microhardness tests, the research addresses existing gaps in knowledge on fine lattice strut elements and their impact on surface morphology and microstructure.

The study revealed that laser energy, power density and strut inclination angle significantly impact the microstructure, geometrical properties and mechanical performance of SLM-produced AlSi10Mg struts. Findings insight enable the optimization of SLM process parameters to produce lattice structures with enhanced surface morphologies, microstructures and mechanical properties, paving the way for applications in medical science and space technologies.

This study uniquely investigates the effects of processing parameters, strut diameter and inclination angle on SLM-fabricated AlSi10Mg struts, focusing on fine lattice strut elements with diameters as small as 200 µm. Unlike existing literature, it delves into the complex correlations among strut size, structural orientation and process parameters to understand their impact on microstructure, geometrical imperfections and mechanical properties. The study provides novel insights that contribute to the optimization of SLM process parameters, moving beyond the typically recommended guidelines from powder or machine suppliers.

Elastic rings served as the elastic supporting elements which have been extensively used in the aeroengines for maneuverable planes with high overloading. However, under extreme conditions, the elastic ring contacts the bearing seat, causing elastic ring failure. Therefore, it is necessary to optimize the matching parameters of the elastic ring in order to suppress the occurrence of elastic ring failure under harsh working conditions.

In this paper, a rotor system supported by elastic rings is researched and a multi-objective parameter matching method of elastic ring is proposed, considering the elastic ring failure, rotor system’s frequency forbidden zone and rotor system’s dynamic response. Then, the particle swarm optimization algorithm is used to dynamically constrain the parameter matching space and obtain the ideal solution for the elastic ring parameter matching.

By analyzing the elastic ring’s matching results (different unbalanced forces and disk masses), the relationship between the trend of Pareto front changes and rotor system parameters is studied. In addition, the rotor system’s dynamic characteristics before and after parameter matching are analyzed.

This article provides guidance for the design of elastic rings by matching the parameters of elastic rings.

Traction applications, e.g. the IMs are mainly operated by field-oriented control (FOC). This control technique requires an accurate knowledge of the machine’s parameters, such as the main inductance, the leakage inductances and the stator and rotor resistance. The accuracy of the parameters influences the precision of the calculated rotor flux and the rotor flux angle and the decoupling of the machine’s equations into the direct and quadrature coordinate system (dq-components). Furthermore, the parameters are used to configure the controllers of the FOC system and therefore influence the dynamic behavior and stability of the control.

In this paper, three different methods to calculate the machine’s parameters, in an automated and rapid procedure with minimal measuring expenditure, are analyzed and compared. Moreover, a method to configure a control that reduces the overall Ohmic losses of the machine in every torque speed operation point is presented. The machine control is configured only with the identified machine parameter.

Simulations and test bench measurements show that the evolutionary strategy is able to identify the electrical parameters of the machine in less time and with low error. Moreover, the controller is able to control the torque of the machine with a deviation of less than 2 per cent.

The most significant contribution of the research is the potential to identify the machine parameter of an induction motor and to configure an accurate control with these parameters.

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

High precision assembly processes using industrial robots require the process parameters to be tuned to achieve desired performance such as cycle time and first time through rate. Some researchers proposed methods such as design-of-experiments (DOE) to obtain optimal parameters. However, these methods only discuss how to find the optimal parameters if the part and/or workpiece location errors are in a certain range. In real assembly processes, the part and/or workpiece location errors could be different from batch to batch. Therefore, the existing methods have some limitations. This paper aims to improve the process parameter optimization method for complex robotic assembly process.

In this paper, the parameter optimization process based on DOE with different part and/or workpiece location errors is investigated. An online parameter optimization method is also proposed.

Experimental results demonstrate that the optimal parameters for different initial conditions are different and larger initial part and/or workpiece location errors will cause longer cycle time. Therefore, to improve the assembly process performance, the initial part and/or workpiece location errors should be compensated first, and the optimal parameters in production should be changed once the initial tool position is compensated. Experimental results show that the proposed method is very promising in reducing the cycle time in assembly processes.

The proposed method is practical without any limitation.

The proposed technique is implemented and tested using a real industrial application, a valve body assembly process. Hence, the developed method can be directly implemented in production.

This paper provides a technique to improve the assembly efficiency by compensating the initial part location errors. An online parameter optimization method is also proposed to automatically perform the parameter optimization process without human intervention. Compared with the results using other methods, the proposed technology can greatly reduce the assembly cycle time.

The purpose of this paper is to propose the grey target decision method based on three-parameter interval grey number for dealing with multi-attribute decision-making problems under uncertain environment.

First, the kernel and ranking method of three-parameter interval grey number are defined, which is the basis of determining the positive and negative bull’s-eye. Next, a new distance measure of three-parameter interval grey number is defined in view of the importance of the “center of gravity” point. Furthermore, a new comprehensive bull’s-eye distance is proposed based on the kernel which integrates the distance between different attributes to the positive and negative bull’s-eye. Then attribute weights are obtained by comprehensive bull’s-eye distance minimum and grey entropy maximization.

The paper provides a grey target decision method based on three-parameter interval grey number and example analysis shows that the method proposed in this paper is more reasonable and effective.

If we have a better understanding of the distribution characteristics of three-parameter interval grey number, it is possible to have a more reasonable measure of the distance of three-parameter interval grey number.

The paper provides a grey target decision method, which can help decision maker deal with multi-attribute decision-making problems under uncertain environment.

This paper proposed the kernel and ranking method of three-parameter interval grey number, and defined a new distance measure of three-parameter interval grey number and proposed a new comprehensive bull’s-eye distance, Furthermore, this paper structured a grey target decision method based on three-parameter interval grey number.

The purpose of this paper is to demonstrate application of the Taguchi method to determine the optimum level of three important parameters (factors) related to the use of a mobile phone for the text message entering task, namely illumination level, noise level and mobile angle that maximizes the performance of mobile phone users.

Three levels of each parameter as available in the literature, except for the mobile angle, were considered. The design of the experiment, as proposed by Genichi Taguchi, was used to conduct nine experiments. A total of 30 male subjects participated in the experimental study. The text message entry task, in the form of Arabic text, was presented to the participating subjects and their performance, measured in terms of mean number of characters entered per minute, was recorded. The signal‐to‐noise (S/N) ratio and the analysis of variance (ANOVA) were employed to investigate the users' performance. Finally, a confirmation test was conducted to verify the validity of the results.

Results showed that, at the illumination level of 475 lux, noise level of 45 dB(A), and mobile angle of 70 degrees, the subjects were quite comfortable, efficient and entered the maximum number of characters in the mobile phone per minute. The noise was found to be the dominant parameter with a contribution of 95.53 percent towards the laid down objective followed by mobile angle, 3.25 percent and illumination level, 0.66 percent.

To the best of the authors' knowledge no study has been conducted in the past to investigate the effect of these parameters on the performance of the mobile phone users. In addition, no attempt has yet been made to find the optimal level of these parameters from a text‐entering viewpoint. The paper represents original research and in the authors' opinion carries significantly important values as it provides new information for those involved in the design of the mobile phone environment.

The purpose of this paper is to evaluate and analyze lube oil performance. The plant experience shows that the oil, even after completion of a number of hours of recommended operation, has some residual life. It is desirable to maximize its use to conserve this scarce resource but avoid failures. At present, continuation or change of the oil is decided, based on the manufacturer's recommendation and experience in the plant. The suggested oil change period is conservative and results in non‐efficient usage of oil. This practice needs refinement to include all possible properties/attributes of oil and use of appropriate procedure to assess its realistic performance.

Parameter profile approach (PPA) is applied to evaluate and analyze the lubricating oil performance parameters.

Physical and chemical properties related to degradation of the lube oil are considered as lubricating oil performance parameters. The value of these performance parameters from time to time, obtained by analyzing samples drawn from the system, are analyzed through PPA. In this approach, mean, median, performance parameter index and time performance index are defined and evaluated at different time intervals. The suggested procedure is illustrated by means of an example.

The suggested procedure will be helpful for the maintenance personnel in planned oil replacement, and in identifying the weak oil properties in respect of the identified lube oil performance parameters.

Emitter parameter estimation via signal sorting is crucial for communication, electronic reconnaissance and radar intelligence analysis. However, due to problems of transmitter circuit, environmental noises and certain unknown interference sources, the estimated emitter parameter measurements are still inaccurate and biased. As a result, it is indispensable to further refine the parameter values. Though the benchmark clustering algorithms are assumed to be capable of inferring the true parameter values by discovering cluster centers, the high computational and communication cost makes them difficult to adapt for distributed learning on massive measurement data. The paper aims to discuss these issues.

In this work, the author brings forward a distributed emitter parameter refinement method based on maximum likelihood. The author’s method is able to infer the underlying true parameter values from the huge measurement data efficiently in a distributed working mode.

Experimental results on a series of synthetic data indicate the effectiveness and efficiency of the author’s method when compared against the benchmark clustering methods.

With the refined parameter values, the complex stochastic parameter patterns could be discovered and the emitters could be identified by merging observations of consistent parameter values together. Actually, the author is in the process of applying her distributed parameter refinement method for PRI parameter pattern discovery and emitter identification. The superior performance ensures its wide application in both civil and military fields.

The purpose of this study is to examine the melting heat transfer of magnetohydrodynamics Casson nanofluid flow with viscous dissipation, radiation, and complete slip effects on a porous stretching sheet. Since, the study of melting heat transfer has mesmerized the attention of scientists and engineers in the sense of its enormous uses in industrial processes, solidification, casting, and technology.

Bejan number and entropy are analyzed. Exploration of irreversibility is modeled using the thermodynamics second law. There is a discussion on thermophoresis and Brownian diffusion along with first-order chemical reactions. Adequate transformations are introduced to convert the controlling partial differential equations to ordinary differential equations. The three-phase Lobatto solvers (bvp5c) are used to obtain numerical solutions of the transmitted equations.

The effects of various factors on temperature, velocity, concentration, Bejan number and entropy rate are shown graphically. The velocity field is enhanced by increasing the melting heat parameter, and it declines for growing magnetic parameters. Temperature is decreased for increasing parametric values of melting heat, porous and Casson parameters. A 7% decrease in the Sherwood distribution is seen when we increase the Brownian motion parameter from 0.1 to 0.2. Similarly, an 11% decrement is found in the Nusselt distribution for increasing the Brinkman number from 0.5 to 1.

Entropy and Bejan number experience dual tendencies whenever the melting heat parameter increases. Nusselt number and skin friction experience the opposite behavior for the increasing values of melting parameter. Sherwood number decreases for the increasing values of melting parameter. The velocity profile is directly related to the melting parameter and inversely related to porous and magnetic parameters. Thermophoresis and Brinkman parameters boost the temperature profile and it is controlled by melting and porous parameters. Some notable fields where the present study is used inevitably are silicon wafering, geothermal energy recovery and semiconductor manufacturing.